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Access to vast amounts of data along with affordable computational powerstimulated the reincarnation of neural networks. The progress could not beachieved without adequate software tools, lowering the entry bar for the nextgenerations of researchers and developers. The paper introduces PyTorchPipe(PTP), a framework built on top of PyTorch. Answering the recent needs andtrends in machine learning, PTP facilitates building and training of complex,multi-modal models combining language and vision (but is not limited to thosetwo modalities). At its core, PTP employs a component-oriented approach andrelies on the concept of a pipeline, defined as a directed acyclic graph ofloosely coupled components. A user defines a pipeline using yaml-based (thushuman-readable) configuration files, whereas PTP provides generic workers fortheir loading, training, and testing using all the computational power (CPUsand GPUs) that is available to the user. The paper covers the main concepts ofPyTorchPipe, discusses its key features and briefly presents the currentlyimplemented tasks, models and components.

PyTorchPipe: a framework for rapid prototyping of pipelines combining language and vision